GitHub - Zaoqu-Liu/SVG: Spatially Variable Genes Detection Methods for Spatial Transcriptomics (original) (raw)

A unified R package for detecting spatially variable genes (SVGs) in spatial transcriptomics data, integrating multiple state-of-the-art methods with optimized performance.

📚 Documentation: https://zaoqu-liu.github.io/SVG/

Overview

SVG detection is a fundamental step in spatial transcriptomics analysis, identifying genes whose expression patterns exhibit significant spatial structure. This package provides:

Four SVG detection methods with a unified interface
Optimized implementations with C++ acceleration
Comprehensive documentation with scientific details
Flexible parameters for method customization

Installation

From R-Universe (Recommended)

Install from R-Universe

install.packages("SVG", repos = "https://zaoqu-liu.r-universe.dev")

From GitHub

Install from GitHub

devtools::install_github("Zaoqu-Liu/SVG")

Or install with dependencies

devtools::install_github("Zaoqu-Liu/SVG", dependencies = TRUE)

From Bioconductor (Coming Soon)

Install from Bioconductor (after acceptance)

BiocManager::install("SVG")

Dependencies

Core dependencies (automatically installed):

Matrix, Rcpp, RcppArmadillo

Optional dependencies for specific methods:

For Delaunay triangulation

install.packages("geometry")

For KNN network

install.packages("RANN")

For nnSVG method (required)

install.packages("BRISC")

For accurate p-values

install.packages("CompQuadForm")

Quick Start

library(SVG)

Load your data

expr_matrix: genes x spots (rows x columns)

spatial_coords: spots x coordinates (x, y)

Unified interface

results <- CalSVG(expr_matrix, spatial_coords, method = "meringue")

Or use method-specific functions

results_meringue <- CalSVG_MERINGUE(expr_matrix, spatial_coords) results_binspect <- CalSVG_binSpect(expr_matrix, spatial_coords) results_sparkx <- CalSVG_SPARKX(expr_matrix, spatial_coords) results_nnsvg <- CalSVG_nnSVG(expr_matrix, spatial_coords)

Get significant SVGs

sig_genes <- results$gene[results$p.adj < 0.05]

Methods

1. MERINGUE (Moran's I with Network)

Spatial autocorrelation using Moran's I with binary adjacency network.

results <- CalSVG_MERINGUE( expr_matrix, spatial_coords, network_method = "delaunay", # or "knn" k = 10, # neighbors for KNN alternative = "greater", # test for clustering adjust_method = "BH" )

Best for: General SVG detection with explicit spatial network

2. Seurat (Moran's I with Distance Weights)

Moran's I using inverse distance squared weights (Seurat default).

results <- CalSVG_Seurat( expr_matrix, spatial_coords, weight_scheme = "inverse_squared", # 1/d^2 (default) adjust_method = "BH" )

Best for: Compatible with Seurat workflow, continuous distance weighting

3. binSpect (Giotto)

Binary spatial enrichment test using Fisher's exact test.

results <- CalSVG_binSpect( expr_matrix, spatial_coords, bin_method = "kmeans", # or "rank" rank_percent = 30, # for rank method do_fisher_test = TRUE )

Best for: Fast computation, robust to outliers

4. SPARK-X

Non-parametric kernel-based test using multiple spatial kernels.

results <- CalSVG_SPARKX( expr_matrix, spatial_coords, kernel_option = "mixture", # or "single" n_threads = 4 )

Best for: Large datasets, diverse pattern types

5. nnSVG

Nearest-neighbor Gaussian processes for spatial modeling.

results <- CalSVG_nnSVG( expr_matrix, spatial_coords, n_neighbors = 10, cov_model = "exponential", n_threads = 4 )

Best for: Statistical rigor, effect size estimation

6. Mark Variogram

Spatial pattern detection using mark variogram from spatstat.

results <- CalSVG_MarkVario( expr_matrix, spatial_coords, r_metric = 5, # distance to evaluate variogram normalize = TRUE )

Best for: Different perspective on spatial patterns

Method Comparison

Method	Principle	Speed	Scalability	Effect Size
MERINGUE	Moran's I (network)	Fast	Medium	Moderate
Seurat	Moran's I (1/d²)	Fast	Medium	Moderate
binSpect	Fisher test	Very Fast	High	Odds ratio
SPARK-X	Kernel test	Fast	Very High	No
nnSVG	Gaussian process	Slow	Medium	Yes (prop_sv)
MarkVario	Variogram	Moderate	Medium	Variogram value

Output Columns

All methods return a data.frame with unified column names:

Column	Description
gene	Gene identifier
p.value	Raw p-value
p.adj	Adjusted p-value (BH/FDR)

Method-specific columns:

Method	Additional Columns
MERINGUE	observed, expected, sd, z_score
Seurat	observed, expected, sd, rank
binSpect	estimate (odds ratio), score
SPARK-X	stat_, pval_ (per kernel)
nnSVG	sigma.sq, tau.sq, phi, prop_sv, LR_stat
MarkVario	r.metric.value, rank (no p-values)

Tutorials

Tutorial	Description
Introduction	Complete guide with all methods, benchmarks, and visualizations

Examples

Basic Workflow

library(SVG)

Simulate data

set.seed(42) n_spots <- 500 n_genes <- 100

Expression matrix (genes x spots)

expr <- matrix(rpois(n_genes * n_spots, lambda = 10), nrow = n_genes) rownames(expr) <- paste0("gene_", 1:n_genes) colnames(expr) <- paste0("spot_", 1:n_spots)

Add spatial pattern to first 10 genes

coords <- cbind(x = runif(n_spots, 0, 100), y = runif(n_spots, 0, 100)) rownames(coords) <- colnames(expr)

Create spatial gradient for first genes

for (i in 1:10) { expr[i, ] <- expr[i, ] + round(coords[, 1] / 10) }

Run SVG detection

results <- CalSVG(expr, coords, method = "meringue")

Top SVGs

head(results)

Significant genes

sig_genes <- results$gene[results$p.adj < 0.05] print(sig_genes)

Integration with SpatialExperiment

library(SpatialExperiment) library(SVG)

From SpatialExperiment object

spe <- readRDS("your_spe.rds")

expr_matrix <- as.matrix(logcounts(spe)) spatial_coords <- spatialCoords(spe)